Comment by verhash | Hacker News Reader

Comment by verhash | Hacker News Reader

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Here “exclusion” just means a deterministic reject / abstain decision applied after a model has already produced candidates. Nothing is generated, ranked, or sampled here. Given a fixed set of candidate outputs and a fixed set of verified constraints, the mechanism decides which candidates are admissible and which are not, in a way that is replayable and binary. A candidate is either allowed to pass through unchanged, or it is excluded from consideration because it violates constraints beyond a fixed tolerance.

In practical terms: think of it as a circuit breaker, not a judge. The model speaks freely upstream; downstream, this mechanism checks whether each output remains within a bounded distance of verified facts under a fixed rule. If it crosses the threshold, it’s excluded. If none survive, the system abstains instead of guessing. The point isn’t semantic authority or “truth,” it’s that the decision process itself is deterministic, inspectable, and identical every time you run it with the same inputs.

You really really need to be upfront in the first paragraph or your docs that you are talking about the inner workings of LLMs and other machine learning stuff

Failing that, at least mention it here

LLMs are probabilistic by nature. They’re great at producing fluent, creative, context-aware responses because they operate on likelihood rather than certainty. That’s their strength—but it’s also why they’re risky in production when correctness actually matters. What I’m building is not a replacement for an LLM, and it doesn’t change how the model works internally. It’s a deterministic gate that runs after the model and evaluates what it produces.

You can use it in two ways. As a verification layer, the LLM generates answers normally and this system checks each one against known facts or hard rules. Each candidate either passes or fails—no scoring, no “close enough.” As a governance layer, the same mechanism enforces safety, compliance, or consistency boundaries. The model can say anything upstream; this gate decides what is allowed to reach the user. Nothing is generated here, nothing inside the LLM is modified, and the same inputs always produce the same decision. For example, if the model outputs “Paris is the capital of France” and “London is the capital of France,” and the known fact is Paris, the first passes and the second is rejected—every time. If nothing matches, the system refuses to answer instead of guessing.

You are going so deep with abstract terms that your text becomes a special shorthand you think is clear but is anything but clear.

Stop talking about “exclusion” and “pressure” etc and use direct words about what is happening in the model.

Otherwise, even your attempts at explaining what you have said need more explanation.

And as the sibling comment points out, start by stating what you are actually doing, in concrete not “the math is the same so I assume you can guess how it applies if you happen to know the same math and the same models” terms. Which is asking everyone else, most anyone, to read your mind, not your text.

There is a tremendous difference between connections you see that help you understand, vs. assuming others can somehow infer connections and knowledge they don’t already have. The difference between an explanation and incoherence.

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